import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
from joblib import dump

from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from joblib import dump

class ClassIfication(object):
    def __init__(self):
        self.df = pd.read_csv('daily.csv')
        
    def get_conditions(self):
        """分类前准备
        """
        
        # 计算收益和风险比例
        df = self.df
        df['max_ratio'] = df['max_close'] / df['the_close']
        df['min_ratio'] = df['min_close'] / df['the_close']
        
        # 自动划分高低阈值
        high_return_threshold = df['max_ratio'].quantile(0.4) # 前40%定义为高收益
        higd_risk_threshol = df['min_ratio'].quantile(0.4) # 前40%定义为高风险
        
        # 生成分类标签
        conditions = [
            (df['max_ratio'] >= high_return_threshold) & (df['min_ratio'] <= higd_risk_threshol),
            (df['max_ratio'] >= high_return_threshold) & (df['min_ratio'] > higd_risk_threshol),
            (df['max_ratio'] < high_return_threshold) & (df['min_ratio'] > higd_risk_threshol),
            (df['max_ratio'] < high_return_threshold) & (df['min_ratio'] <= higd_risk_threshol),
        ]
        
        labels = ['高风险高收益', '高收益低风险', '低收益低风险', '低收益高风险']
        
        df['category'] = np.select(conditions, labels, default='位置')
        # 标签选择
        features = df[['eps', 'total_revenue_ps', 'undist_profit_ps', 'gross_margin', 'fcff', 'fcfe', 'tangible_asset', 'bps', 'grossprofit_margin', 'npta', 'roic']]
        
        # 标签编码
        le = LabelEncoder()
        df['category_encoded'] = le.fit_transform(df['category'])
        
        # 标准化数据
        scaler = StandardScaler()
        X = scaler.fit_transform(features)
        y = df['category_encoded']
        
        # 划分训练集与测试集
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=24)
        dump(scaler, 'feature_scaler.joblib')
        return X_train, X_test, y_train, y_test, le
    
    def knn_utils(self, X_train, X_test, y_train, y_test, le):
        knn = KNeighborsClassifier(n_neighbors=3)
        knn.fit(X_train, y_train)
        
        # 预测与评估
        y_pred = knn.predict(X_test)
        print(classification_report(y_test, y_pred, target_names=le.classes_))
        dump(knn, 'knn_classifien.joblib')
        dump(le, 'label_encoder.joblie')
        
    def svc_utils(self, X_train, X_test, y_train, y_test):
        """支持向量机
        """
        svc = SVC()
        svc.fit(X_train, y_train)
        predict = svc.predict(X_test)
        print("accuracy_score: %.1lf" % accuracy_score(predict, y_test))
    
        
    
if __name__ == '__main__':
    ci = ClassIfication()
    X_train, X_test, y_train, y_test, le = ci.get_conditions()
    ci.knn_utils(X_train, X_test, y_train, y_test, le )
    # ci.svc_utils(X_train, X_test, y_train, y_test)